Staff Engineer, Machine Learning

Cariad, Inc.•Mountain View, CA

About The Position

We are CARIAD, an automotive software development team with the Volkswagen Group. Our mission is to make the automotive experience safer, more sustainable, more comfortable, more digital, and more fun. To achieve that we are building the leading tech stack for the automotive industry and creating a unified software platform for over 10 million new vehicles per year. We’re looking for talented, digital minds like you to help us create code that moves the world. Together with you, we’ll build outstanding digital experiences and products for all Volkswagen Group brands that will transform mobility. Join us as we shape the future of the car and everyone around it. Role Summary: The Staff Engineer, Machine Learning, is responsible for leading the development of a single-stage, end-to-end driving model for the ADAS system. This role designs, trains, and fine-tunes reinforcement learning-based models using a world-model simulation environment and leverages multi-modal sensor inputs such as camera and radar data to generate driving trajectories. This role focuses on bridging advances in multi-modal foundation models with the practical challenges of real-time, safety critical embedded deployment. The Staff Engineer, Machine Learning ensures the model is robust, generalizes well, and meets safety standards across a wide range of driving scenarios. This role works closely with embedded engineers, data engineers, and MLOps/DevOps engineers, to create a scalable, high-performance system that delivers real-world impact.

Requirements

Strong software engineering skills, including the ability to write clean, maintainable, and testable production-quality code
Strong analytical and debugging skills, with the ability to evaluate tradeoffs and select appropriate technical solutions
Ability to independently work on moderately complex technical problems, exercising sound judgment in ambiguous problem spaces
Strong written and verbal communication skills, with the ability to clearly explain complex technical concepts to diverse audiences
Ability to collaborate effectively with multiple teams, including working across geographies and time zones
Strong software engineering skills, including the ability to write clean, maintainable, and testable code
Strong analytical and debugging skills applied to machine learning and data-driven systems
Ability to work on moderately complex technical problems with guidance from more experienced engineers
Clear written and verbal communication skills for collaborating with cross-functional partners
Deep Learning expertise with a strong command of CNNs, transformers, spatio-temporal models, and advanced topics such as foundation models and LLMs
Hands on experience with machine learning frameworks such as PyTorch (or equivalent)
Reinforcement learning experience, including training agents in simulation environments
Computer vision experience applying modern deep learning techniques such as CNNs, DETR, and vision transformers to real-world problems
Experience or strong familiarity with state-of-the-art AD/ADAS systems, including end2end driving models, VLAMs, and world models.
Strong applied foundation in core machine learning principles, with the ability to translate theory into practical model development and evaluation
6+ years of experience in Applied machine learning or deep learning
3+ years of experience reinforcement learning, computer vision, or AD/ADAS systems.
Strong candidates with equivalent industry experience will be considered
Master’s in Computer Science, Robotics, Electrical Engineering, Applied Mathematics, or a related field

Nice To Haves

Familiarity with deep learning model optimization techniques, such as quantization, pruning, and hardware-aware optimization
Familiarity with inference frameworks such as TensorRT and ONNX Runtime
Experience working with simulation frameworks for ADAS development
Experience with multi-modal machine learning models, including camera and radar fusion and other multi-modal architectures such as VLAMs
Understanding of automotive safety considerations relevant to machine learning–based ADAS systems
PhD in Computer Science, Robotics, Electrical Engineering, Applied Mathematics, or a related field

Responsibilities

Research, evaluate, and select promising single-stage, end-to-end ADAS model approaches and architectures
Design and train state-of-the-art end-to-end machine learning models for the ADAS stack
Define and evolve single-stage training strategies for end-to-end models in collaboration with data engineering and MLOps teams
Train models using reinforcement learning approaches within simulation or world-model environments and reinforcement learning frameworks
Work with real and synthetic multi-modal sensor data (camera, radar, lidar) to design models that effectively leverage all available data modalities
Ensure models generalize across diverse driving scenarios and operational conditions
Evaluate and benchmark models against real-world driving use cases using scalable evaluation pipelines
Collaborate with embedded engineering teams to support model optimization, deployment on embedded hardware, and system integration
Support model integration, performance tuning, and issue resolution during deployment and validation phases
Partner with embedded, data, and platform teams to align model development with system constraints and deployment requirements
Share technical insights and lessons learned to improve overall ADAS machine learning development practices